Results

Causal Factors. Causal assessments were completed
for 1239 (96.5 percent) of the drivers in the sample. There was insufficient
data to complete causal assessments for 45 of the drivers. Of the 1284
drivers contained in the database, 507 (40.3 percent) were assessed as
not contributing to crash causation. To demonstrate the relative importance
of causal factor types, drivers who did not contribute to causation (507)
and unknown values (45) were eliminated from the distribution. Proportions
were then recomputed using the number of drivers who contributed to causation
(732) as the denominator in subsequent calculations. The most frequently
assigned causal factor groups are described below and shown in Figure
2.

DRIVER INATTENTION. The most dominant component of the causal
factor pattern was driver inattention. As defined for this effort, driver
inattention indicated a lack of focus on the required field of view
(typically forward). This definition encompassed both of the driver
inattention and driver distraction categories as defined in the earlier
Indiana Tri-Level study (Treat, et al, 1979). Inattention was noted
as the sole causal factor for 16.7 percent of the drivers who contributed
to crash causation and was noted as the primary causal factor in combination
with other contributory factors for 5.2 percent of the drivers. This
factor was also cited as a contributory factor in combination with other
primary factors for 0.8 percent of the drivers contributing to causation.

VEHICLE SPEED. The second largest component of the causal factor
pattern was the vehicle speed factor. These assignments typically reflected
circumstances in which the driver was exceeding the speed limit and
the absolute vehicle velocity contributed to crash causation. It should
be noted, however, that this causal factor was assigned in a small number
of crashes where the vehicle's travel speed was at or below the posted
speed limit. In these situations, the travel speed was inappropriate
for prevailing weather/roadway conditions and contributed to a pre-crash
loss of vehicle control (i.e., too fast for conditions).

Vehicle speed was assigned as the sole causal factor for 6.8 percent
of the drivers who contributed to crash causation and was assigned as
the primary factor in combination with other contributory factors for
3.8 percent of the drivers who contributed to causation. In addition,
this factor was cited as a contributory factor in combination with other
primary factors for 8.1 percent of the drivers.

ALCOHOL IMPAIRMENT. Alcohol impairment was the third largest
component of the causal factor pattern. Driving while impaired by alcohol
was the sole causal factor for 6.0 percent of the drivers who contributed
to crash causation and was noted as the primary factor in combination
with other contributory factors for 11.1 percent of the drivers who
contributed to causation. In addition, alcohol impairment was cited
as a contributory factor in combination with other primary factors for
1.1 percent of the drivers.

PERCEPTUAL ERRORS. The fourth most frequently assigned causal
factor involved perceptual errors associated with intersection crashes.
Two specific scenarios were noteworthy: (1) The subject driver checked
for approaching traffic, did not see the other crash-involved vehicle
(e.g., looked, did not see), and then attempted to cross or turn at
the intersection. This factor was noted as the sole causation mechanism
for 8.9 percent of the drivers who contributed to crash causation. (2)
The driver checked for approaching traffic, saw the other vehicle, but
then either misjudged the distance to that vehicle or misjudged the
approach velocity of that vehicle (e.g., accepted inadequate gap to
other vehicle). This factor was noted as the sole causation mechanism
for 6.0 percent of the drivers who contributed to causation.

DECISION ERRORS. The primary scenario in this group involved
subject drivers who attempted to turn or cross with an obstructed view
(4.7 percent). While these situations typically reflected intersection
crashes, there were a number of collisions which occurred at non-intersection
locations (e.g., driver attempted to cross the roadway from a private/commercial
driveway or attempted to turn into/exit a private/commercial driveway).

Additional causal factor types in this category included (1) violated
a red traffic signal (2.6 percent), (2) attempted to beat a phasing
signal (2.1 percent), and (3) violated a stop sign (0.7 percent). The
total contribution of this category was 10.1 percent with all of the
assignments occurring as primary/sole assignments.

INCAPACITATION. Drivers who fell asleep (4.4 percent) or experienced
a seizure/heart attack/blackout (2.0 percent) also contributed to the
causal factor pattern. All of the assignments in this category were
made as primary/sole assignments (i.e., no contributory factors noted).

These six causal factor groups were assigned as primary (sole) factors
for 60.9 percent of the drivers contributing to crash causation. These
same factors were assigned as primary factors in combination with other
contributing factors for an additional 20.2 percent of the drivers who
contributed to crash causation. Thus, as primary assignments, these
factors were assigned to 81.1 percent of the drivers who contributed
to causation.

NOTE: Due to multiple causal factor assignments, proportions
for individual causal factors add to more than 100.0.

Figure 2: Six Most frequently Assigned
Causal Factor Groups

Crash Problem Types

In this multivariate analysis, important driver demographic/behavioral
characteristics and crash situation descriptors associated with seven
crash types were identified. The process involved eight major steps:

1. Produced and reviewed frequency distributions
for each of the 203 variables contained in the combined NASS CDS/UDA
data file.

2. Selected a set of 59 "Pattern"
variables containing information useful for describing crashes in
terms of UDAs and other crash, driver, vehicle, and road environment
factors. Variables were selected from the following sources:

4. Recoded NASS crash types (provided in Figure
3) to simplify and improve the analysis. Crash types were redefined
into seven classes with operational differences that were likely
to be associated with driver behavior/performance as follows:

5. Determined unweighted frequencies for each
of the 59 pattern variables, treating each driver/vehicle as a unit
of analysis. Cross tabulations of unweighted observations of each
pattern variable with crash type were then constructed.

6. Calculated a relative involvement index to
assess the over-and-under representation of each profile variable
within each crash type. Tables were prepared showing the frequency,
percentage, and relative involvement in six of the 59 pattern variables
within the seven defined crash types.

Figure 3: Crash Types as Identified in the
NASS Program.

7. Selected a limited set of six "key"
profile variables (from the original set of 59 pattern variables)
to characterize crash scenarios within crash types. The key variables
which frequently had high indices of over-representation included
crash cause, BAC test result, primary behavior source, necessary
UDA, travel speed, and first UDA in sequence. Another set of more
general variables including driver age, sex road surface condition,
and lighting was also examined to further characterize specific
scenario types.

8. Determined the most frequent scenarios within
each crash. In general, it was noted that combinations of four of
the six key variables noted in the preceding step resulted in the
most homogenous and distinctive scenario groupings. Specifically,
BAC test result and travel speed were excluded from the cross-tabulations.
For Crash Type 3: Same Direction; Rear End crashes, however, it
was necessary to include the travel speed variable to achieve adequate
distinction between the scenario types.

A prioritized listing of crash problem types identified
by this analysis sequence is provided in Table 1. The 23 problem types
shown in this table comprised 43.2 percent of the UDA crash sample. These
same problem types contributed to an additional 25.2 percent of the crashes
in the sample when they were combined with a broad range of other factors.
Therefore, the problem types in Table 1 contributed to more than two-thirds
of the UDA sample crashes.

Table 1

Prioritized Listing of Crash Problem
Types

Crash Type

Problem Type

% of UDA Sample

3. Same Direction, Rear End

1. Driver Inattention - Mid Range
Speeds

2. Driver Inattention - Low Range
Speeds

3. Driver Inattention - High
Range Speeds

4. Following Too Closely - High
Range Speeds

5.6

2.5

2.4

2.4

4. Turn, Merge, Path Encroachment

1. Looked, Did Not See

2. Accepted Inadequate Gap To
Other Vehicle

3. Turned With Obstructed View

4. Driver Inattention/TCD Violation

4.1

3.3

2.3

2.3

2. Single Driver, Right or
Left Roadside Departure With Traction Loss

1. Excessive Vehicle Speed

2. DUI/DWI With Excessive Speed

3. DUI/DWI

2.3

1.6

1.6

1. Single Driver, Right or
Left Roadside Departure Without Traction Loss

1. Driver Fatigue

2. Driver Inattention

3. DUI/DWI

1.7

1.6

1.5

6. Intersecting Paths, Straight
Paths

1. Looked, Did Not See

2. Driver Inattention/TCD Violation

3. Crossed With Obstructed View

1.6

1.3

1.2

5. Same Trafficway, Opposite
Direction

1. Driver Inattention

2. Lost Directional Control

3. Excessive Vehicle Speed

0.9

0.9

0.8

7. Other, Miscellaneous

1. Excessive Vehicle Speed

2. Following Too Closely

3. Sudden Deceleration

0.5

0.4

0.4

Total

43.2

Key characteristics of crash problem types are summarized
in Tables 2 through 8. The presentation sequence is as follows:

Table No.

Crash Type

Problem Type

% of UDA Sample

2

Same Direction, Rear End

1-4

12.9

3

Turn, Merge, Path Encroachment

1-4

12.0

4

Single Driver, Roadside Departure
With Traction Loss

1-3

5.5

5

Single Driver, Roadside Departure
Without Traction Loss

1-3

4.8

6

Intersecting Paths, Straight Paths

1-3

4.1

7

Same Trafficway, Opposite Direction

1-3

2.6

8

Other, Miscellaneous

1-3

1.3

Total

43.2

Table 2

Same Direction, Rear End Crashes (Problem
Types 1-4)

Crash Problem Type

Key Characteristics

1. Driver Inattention -

Mid Range Travel Speeds

5.6 Percent of UDA Sample

Subject driver was inattentive to the driving
task and struck the rear of a lead vehicle.

Two scenarios were identified. In the most frequently
occurring scenario (76 percent), the subject driver was traveling
on urban/suburban surface street and in the second scenario the
subject driver was traveling on an entrance ramp to an expressway/interstate
roadway.

Nearly all crashes occurred during daylight hours,
in clear weather conditions, and in heavy traffic densities.

Drivers in the ramp scenario were inattentive
as a result of focusing on traffic in the through lanes. Inattention
mechanisms for drivers on surface streets were varied and included
looking at buildings (5.3 percent), adjusting cassette player
(5.3 percent), conversing with passengers (15.8 percent), looking
at approaching traffic (5.3 percent), looking in rear view mirror
(26.1 percent), focusing on internal thought processes (5.3 percent).

Younger female drivers (<35 years) were over-represented
in the age distribution (42.9 percent).

Most drivers in this crash type did not attempt
to shift crash responsibility.

3. DUI/DWI Crashes

1.5 Percent of UDA

Sample

Subject driver exited the roadway as a result
of a DUI/DWI circumstance.

Most of the crashes occurred on local or collector
roadways during periods of darkness with the highest proportion
occurring between midnight and 5 am (53.6 percent).

Crashes were often associated with vehicle speed.
Specifically, the driver was exceeding the speed limit in 50.0
percent of these crashes.

Younger male drivers (<35 years) were over-represented
(42.9 percent) as were male drivers between the ages of 35-54
(35.7 percent).

Drivers typically did not admit to consuming
alcoholic beverages prior to crash occurrence.

Table 6

Intersecting Paths, Straight Paths Crashes
(Problem Types 1-3)

Crash Problem Type

Key Characteristics

1. Looked, Did Not See

1.6 Percent of UDA

Sample

All crashes occurred at intersection locations
where the subject vehicle was controlled by a stop sign.

Approach trajectories were initially separated
by 90 degrees.

Both drivers intended to proceed straight through
the intersection.

The other crash-involved vehicle was typically
approaching from the subject driver's right (71.4 percent). The
subject driver did not see this vehicle and accelerated into the
intersection.

Older drivers were over-represented with 35.7
percent of the drivers exceeding the age of 70 and 42.8 percent
exceeding the age of 55.

Drivers between 35 and 54 years of age appeared
to be involved as a result of using inappropriate traffic scanning
techniques. Younger drivers (<35 years ) were involved as a
result of performing perfunctory traffic checks.

Drivers did not attempt to shift crash responsibility.

2. Driver Inattention/

TCS Violation

1.3 Percent of UDA

Sample

All crashes occurred at intersection locations
that were typically controlled by traffic signals (80 percent).

All crashes occurred during daylight hours and
during periods of moderate to moderately heavy traffic densities.

Sample size was limited, but males in the 35-54
year age group appeared to be over-represented.

Drivers did not attempt to shift crash responsibility.

Table 7

Same Trafficway, Opposite Direction
Crashes (Problem Types 1-3)

Crash Problem Type

Key Characteristics

1. Driver Inattention

0.9 Percent of UDA

Sample

Trajectories of involved vehicles were initially
180 degrees opposed.

The subject driver became inattentive to the
driving task and allowed the subject vehicle to drift into the
opposing traffic lane.

The subject vehicle most frequently struck the
side of the other vehicle (36.4 percent) or was struck in the
side by the other vehicle (33.3 percent). The remaining crashes
were either head-on configurations or off-set frontal configurations.

Most crashes occurred during daylight hours and
clear weather conditions (87.5 percent) and during periods of
light traffic densities.

Clinical sample size was insufficient to establish
the range of situational characteristics. All the drivers in the
sample, however, were less than 35 years of age.

Table 8

Other, Miscellaneous Crashes (Problem
Types 1-3)

Crash Problem Type

Key Characteristics

1. Excessive Speed

0.5 Percent of UDA

Sample

Subject vehicles were involved in a wide array
of unusual impact configurations.

The common thread tying these crashes together
was involvement of the subject vehicle due to excessive speed.

The clinical sample size was insufficient to
establish the range of situational characteristics or demographic
characteristics.

2. Following Too Closely

0.4 Percent of UDA

Sample

Subject vehicles were involved in a wide array
of unusual impact configurations.

The subject vehicle's crash involvement could
be traced to following too closely behind a lead vehicle.

The clinical sample size was insufficient to
establish the range of situational characteristics or demographic
characteristics.

3. Sudden Deceleration

0.4 Percent of UDA

Sample

Subject vehicles were lead vehicles that decelerated
suddenly due to a non-contact vehicle crossing its intended travel
path.

Sudden deceleration steering/braking inputs resulted
in a misalignment between the lead and following vehicles such
that a nominal rear end crash configuration was changed to a front
to side impact configuration.

The clinical sample size was insufficient to
establish the range of situational characteristics or demographic
characteristics.

There were several other interesting findings as a result
of these analyses. Some of these are described below.

Despite the fact that 732 drivers committed some behavioral error
or unsafe driving act, only 418 drivers (57 percent) were charged with
any violation by the police. Of the drivers receiving citations from
the police, 18 percent were for failure to yield, 17 percent for
driving while impaired, 10 percent for violating stop signs or traffic
signals, 7 percent for reckless driving, and 4 percent for speeding
violations.

Almost one-third of the drivers in the sample (29 percent) indicated
that they were unaware of the impending collision and did not recognize
any need for evasive action.

Close to one-third of the turning/intersection crashes (32 percent)
occurred at locations where there were no traffic control devices reflecting
the large number of cases where drivers were turning into private driveways
or commercial accesses.

Approximately 79 percent of the primary unsafe driving acts reflected
a deliberate intent of the driver to engage in that action. Most of
the unintentional acts were associated with "driver inattention"
and "looked but did not see" behavioral errors.

The source of the driver behavioral errors in these crashes was distributed
as follows: